Applied AI Product Engineer

As an Applied AI Product Engineer, you will build internal products and integrations that help content, product, and research teams design, evaluate, and launch AI-powered experiences. You will work across the product lifecycle, from understanding ambiguous user problems and defining a technical approach to implementation, launch, and iteration.
This is a hands-on engineering role. Most of your time will be spent designing, coding, testing, and operating solutions, with enough internal user interaction to ensure we are solving the right problems. A core part of the role is turning repeated technical support and manual workflows into reliable self-service tools. The work spans AI product evaluation tooling, data and analytics workflows, and new applied-AI prototypes.
What You’ll Do

Collaborate closely with both technical and non-technical stakeholders to deeply understand user problems, desired outcomes, and inherent constraints before architecting and selecting optimal solutions.
Design, develop, and implement end-to-end internal products, robust integrations, and efficient data workflows that streamline operations and enhance capabilities.
Transform experimental prototypes and one-off solutions into scalable, reliable, and reusable capabilities that serve a broad range of internal users.
Develop sophisticated evaluation and feedback mechanisms for AI-powered workflows, ensuring continuous improvement and alignment with strategic goals.
Apply rigorous production engineering practices, including comprehensive testing, robust observability, actionable error handling, and safe failure mechanisms, to ensure system stability and reliability.
Utilize AI coding tools strategically and thoughtfully, always reviewing and validating all generated work to maintain code quality and integrity.
Measure and analyze key metrics such as adoption rates, quality improvements, time saved, and reduction in manual support efforts to demonstrate impact.
Create clear, comprehensive documentation and intuitive onboarding paths that empower users and fellow engineers to operate independently and efficiently.
Take ownership of and prioritize multiple technical workstreams, making informed trade-offs based on potential impact and complexity to achieve strategic objectives.

Must-Have Qualifications

Bachelor’s degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
6+ years of experience building production software in roles such as software engineering, product engineering, ML engineering, or a hands-on technical solutions capacity.
Demonstrated experience independently owning the full lifecycle of scoped technical products, from addressing ambiguous problems through design, implementation, launch, and iteration.
Proficiency in one or more general-purpose programming languages such as Python, JavaScript/TypeScript, Java, PHP/Hack, C++, or similar.
End-to-end product engineering: experience independently designing, building, testing, launching, and operating production software involving APIs, backend services, data systems, or full-stack products.
Proven experience applying production engineering practices such as automated testing, observability, actionable errors, and safe failure handling.
Experience utilizing data and metrics to evaluate outcomes and effectively communicating complex technical concepts and trade-offs to both technical and non-technical stakeholders.

Nice-to-Have Qualifications

Experience building or integrating AI/ML-enabled products, sophisticated evaluation systems, or agentic workflows.
Prior experience in building internal tools, developer platforms, or reusable self-service capabilities that empower other teams.
Strong product judgment or demonstrated experience influencing product roadmaps by translating user feedback into actionable engineering requirements.
Demonstrated experience using AI-assisted development or LLM-based systems to significantly improve engineering velocity or redesign operational workflows, while independently reviewing and validating the resulting work.
Experience contributing to shared codebases using best practices such as version control, rigorous code review, automated testing, and documented engineering practices.
Experience applying responsible AI practices, including risk assessment, bias mitigation, and thorough quality and accuracy reviews.